In recent years, demand-side management (DSM) has attracted increasing attention in balancing the demand and supply of electricity for future smart grids. Particularly, many researchers consider DSM with dual-decomposition for which the theoretical properties are based on Lagrangian relaxation. It has been proven that the optimal profile of generation and consumption using DSM with dual-decomposition can be obtained. However, the convergence error and the existing range of the optimal price have not been analyzed sufficiently, nevertheless the success of dual decomposition centers on finding an good solution. In this paper, we consider the expanded electricity grid model based on Atzeni and Samadi's model. We introduce a day-ahead pricing algorithm, which is an extension of Samadi's algorithm, and we analyze the error and the range. Finally, we show the main parameters that have an impact on price through this theoretical analysis, that is, the maximum sell and purchase value have an impact and the maximum values of other parameters do not.
In this paper, we present a self-triggered control scheme for discrete-time average consensus problems. In the conventional time-triggered control, each agent has to periodically exchange information with its neighbor agents and update their inputs. On the other hand, in the self-triggered control, they can avoid such inefficient usage of computational resources by exchanging information and updating their inputs only when necessary. We show that the proposed discrete-time consensus protocol with synchronized self-triggered control achieves an average consensus based on the analysis of gradient-like iterative algorithms.
A method for the operation plan optimization of energy networks is reported on in this paper. The method optimizes the operation of heat source equipment and pump. Its main feature is to consider the loss of pressure in pipes. All equations are linearized and the optimization is calculated by using MILP. This method is applied to energy networks with two connected areas. Those areas have facilities with different efficiency and different demand properties. The results of optimization verified the energy saving effect, in comparison with discrete areas.
A few years ago, the authors proposed a nature-inspired metaheuristic concept, the spiral optimization algorithm, which was inspired by spiral phenomena in nature. The principal idea of the algorithm is to utilize spiral trajectories generated by multiple generalized spiral models for search applications. The generalized spiral model is composed of a spiral matrix defined by a composite rotation matrix and a convergence rate parameter. The setting of the spiral matrix with each initial point placement is important for its search performance, because it characterizes each spiral trajectory. This paper proposes 1) the concept of periodic descent directions for a spiral trajectory that is appropriate for optimization; 2) sufficient conditions, with examples, for the generalized spiral model to generate the periodic descent directions; 3) a setting method for the composite rotation matrix with initial search points to satisfy the conditions in the algorithm; and 4) a method for setting the convergence rate parameter to utilize the periodic descent directions effectively for its search performance. The effectiveness of the proposed method is confirmed by conducting numerical experiments under various conditions.
In this study, we propose a method for determining the classroom seating arrangements considering relationships between one student and the other students sitting around him or her. The method for determining the classroom seating arrangements is constructed based on our proposed genetic algorithm. In order to determine the optimal classroom seating arrangements, the genetic algorithm is applied on the basis of the questionnaire result of how students feel when they take a class on the seats assigned to them and the analysis of each student's personality. Experiments are carried out in order to verify the effectiveness of the proposed method.